yolov2 [1]} is the second version of yolo, which adopts a series of design decisions from past works with novel concepts to improve speed and precision. In particular: i) it adds a bn layer ahead of each convolutional layer which accelerates the network to get convergence and helps regularize the model; ii) it removes the final fully-connected layers; iii) it adopts an anchor-based prediction mechanism where the anchors are learned with k-means; iv) for localizing smaller objects, it concatenates the higher resolution features with the low-resolution features by stacking adjacent features into different channels; v) it performs a multi-scale training; vi) it proposes a new classification backbone namely Darknet-19, with 19 convolutional layers and five max-pooling layers, which requires fewer operations to process an image yet achieves high accuracy. Furthermore, in the same work, the authors also introduced YOLO9000, which can detect over 9000 object categories in real-time by proposing a joint optimization method to train simultaneously on ImageNet [2]} and COCO [3]} with WordTree, combining data from multiple sources.
